Abstract:
Non-destructive testing & evaluation (NDT&E) plays a vital role in industrial quality control. Among various
NDT&E modalities, active thermal NDT&E gained its importance due to its inherent merits such as remote,
whole-field, fast and quantitative inspection capabilities. Of various thermal NDT&E schemes, recently proposed
pulse compression favorable frequency modulated thermal wave imaging (FMTWI) became popular due to its
enhanced defect detection sensitivity along with improved test resolution. This paper presents noise rejection
capabilities of FMTWI with principal component analysis (PCA) based post-processing schemes. PCA based postprocessing
helps in efficient interpretation of the thermographic data by removing artefacts and producing few
significant images depicting sub-surface defects in the test specimen. The results obtained by PCA can be made
more interpretable by using sparser version of PCA (SPCA). In this paper, SPCA based thermographic data
processing technique is proposed in which SPCA has been considered in two different ways. Firstly it has been
implemented to induce sparsity in empirical orthogonal functions (EOFs) which improves spatial contrast over
the defective regions. Secondly, it has been used to modify principal components (PCs) (time series components)
to obtain resultant images by projecting thermographic data on modified PCs which manages to enhance the
signal to noise ratio (SNR). The sub-surface defect detection capabilities of the proposed methods are studied by
a matched filter based pre-processing scheme which reduces the computational cost and memory usage also. The
performance of the proposed methods has been evaluated on the experimental investigation of the mild steel
specimen having flat bottom holes as defects.
